TrueFoundry helps you to seamlessly manage the entire machine learning lifecycle, from experimentation to deployment and beyond. You can:
Kickstart your machine learning journey by launching a Jupyter Notebook to explore and experiment with your ideas.
Once your model is ready for training, execute a model training job from within the Notebook using the Python SDK. Or you can push your training code to a Github Repository and deploy directly from a public Github repository
Seamlessly log your trained model to the TrueFoundry Model Registry, which is backed by a secure blob storage service like S3, GCS, or Azure Container.
Deploy the logged model as a:
- Real-time API Service: Deploy your model as a real-time API Service to serve predictions in real-time, either from a public Github repository or from a local-machine / notebook
- Batch Inference Service: Deploy your model for batch inference to process large datasets efficiently by deploying it as a Job
- Async Service: Handle requests asynchronously using a queue to store intermediate requests by deploying an Async Service
LLM Testing and Deployment: Evaluate and compare the performance of various LLMs using TrueFoundry's LLM Gateway capabilities. Once you've selected the desired LLM, deploy it with ease using pre-configured settings
LLM Finetuning: Leverage TrueFoundry's LLM finetuning capabilities to tailor LLMs to your specific needs and data.
Updated 2 days ago